A comparison between radial artery compression devices for patent hemostasis after transradial percutaneous interventions.

OBJECTIVES: Patent hemostasis (PH) is essential for preventing radial artery occlusion (RAO) after trans-radial procedures; however, it remains unclear how it should be obtained. The aim of this multicenter randomized study was to evaluate whether the use of an adjustable device (AD), inflated with a pre-determined amount of air (AoA), was more effective than a non-adjustable device (non-AD) for achieving PH, thereby reducing the incidence of RAO. METHODS: We enrolled a total of 480 patients undergoing transradial procedure at 3 Italian institutions. Before the procedure, a modified Reverse Barbeau Test (mRBT) was performed in all patients to evaluate the AoA to be eventually inflated in the AD. After the procedure, patients were randomized into 2 groups: (1) AD Group, using TR-Band (Terumo) inflated with the pre-determined AoA; and 2) non-AD Group, using RadiStop (Abbott). An RBT was performed during compression to demonstrate the achievement of PH, as well as 24 hours later to evaluate the occurrence of RAO. RESULTS: PH was more often obtained in the AD Group compared with the non-AD Group (90% vs 64%, respectively, P less than .001), with no difference in terms of bleedings. RAO occurred more often in the non-AD Group compared with the AD Group (10% vs 3%, respectively, P less than .001). Of note, mRBT was effective at guiding AD inflation and identifying high-risk patients in whom PH was more difficult to obtain. CONCLUSIONS: The use of AD, filled with a predetermined AoA, allowed PH significantly more often compared with non-AD, providing a significantly reduced incidence of RAO.

Remember me - user-centered implementation of working memory architectures on an industrial robot.

The present research is innovative as we followed a user-centered approach to implement and train two working memory architectures on an industrial RB-KAIROS + robot: GRU, a state-of-the-art architecture, and WorkMATe, a biologically-inspired alternative. Although user-centered approaches are essential to create a comfortable and safe HRI, they are still rare in industrial settings. Closing this research gap, we conducted two online user studies with large heterogeneous samples. The major aim of these studies was to evaluate the RB-KAIROS + robot's appearance, movements, and perceived memory functions before (User Study 1) and after the implementation and training of robot working memory (User Study 2). In User Study 1, we furthermore explored participants' ideas about robot memory and what aspects of the robot's movements participants found positive and what aspects they would change. The effects of participants' demographic background and attitudes were controlled for. In User Study 1, participants' overall evaluations of the robot were moderate. Participant age and negative attitudes toward robots led to more negative robot evaluations. According to exploratory analyses, these effects were driven by perceived low experience with robots. Participants expressed clear ideas of robot memory and precise suggestions for a safe, efficient, and comfortable robot navigation which are valuable for further research and development. In User Study 2, the implementation of WorkMATe and GRU led to more positive evaluations of perceived robot memory, but not of robot appearance and movements. Participants' robot evaluations were driven by their positive views of robots. Our results demonstrate that considering potential users' views can greatly contribute to an efficient and positively perceived robot navigation, while users' experience with robots is crucial for a positive HRI.

Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing.

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units-accelerated quantum chemistry in the cloud, artificial intelligence-driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.

Invasive Assessment of Coronary Microcirculation: A State-of-the-Art Review.

A significant proportion of patients presenting with signs and symptoms of myocardial ischemia have no "significant" epicardial disease; thereby, the assessment of coronary microcirculation gained an important role in improving diagnosis and guiding therapy. In fact, coronary microvascular dysfunction (CMD) could be found in a large proportion of these patients, supporting both symptoms and signs of myocardial ischemia. However, CMD represents a diagnostic challenge for two main reasons: (1) the small dimension of the coronary microvasculature prevents direct angiographic visualization, and (2) despite the availability of specific diagnostic tools, they remain invasive and underused in the current clinical practice. For these reasons, CMD remains underdiagnosed, and most of the patients remain with no specific treatment and quality-of-life-limiting symptoms. Of note, recent evidence suggests that a "full physiology" approach for the assessment of the whole coronary vasculature may offer a significant benefit in terms of symptom improvement among patients presenting with ischemia and non-obstructive coronary artery disease. We analyze the pathophysiology of coronary microvascular dysfunction, providing the readers with a guide for the invasive assessment of coronary microcirculation, together with the available evidence supporting its use in clinical practice.

Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task.

A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. However, aspects like the uncanny valley and different technical affinity levels can impede the success of HRI scenarios, which has consequences on the establishment of long-term interaction qualities like trust and rapport. In the present study, we investigate the impact of a humanoid robot on human emotional responses during the performance of a cognitively demanding task. We set up three different conditions for the robot with increasing levels of social cue expressions in a between-group study design. For the analysis of emotions, we consider the eye gaze behavior, arousal-valence for affective states, and the detection of action units. Our analysis reveals that the participants display a high tendency toward positive emotions in presence of a robot with clear social skills compared to other conditions, where we show how emotions occur only at task onset. Our study also shows how different expression levels influence the analysis of the robots' role in HRI. Finally, we critically discuss the current trend of automatized emotion or affective state recognition in HRI and demonstrate issues that have direct consequences on the interpretation and, therefore, claims about human emotions in HRI studies.

Perception is Only Real When Shared: A Mathematical Model for Collaborative Shared Perception in Human-Robot Interaction.

Partners have to build a shared understanding of their environment in everyday collaborative tasks by aligning their perceptions and establishing a common ground. This is one of the aims of shared perception: revealing characteristics of the individual perception to others with whom we share the same environment. In this regard, social cognitive processes, such as joint attention and perspective-taking, form a shared perception. From a Human-Robot Interaction (HRI) perspective, robots would benefit from the ability to establish shared perception with humans and a common understanding of the environment with their partners. In this work, we wanted to assess whether a robot, considering the differences in perception between itself and its partner, could be more effective in its helping role and to what extent this improves task completion and the interaction experience. For this purpose, we designed a mathematical model for a collaborative shared perception that aims to maximise the collaborators' knowledge of the environment when there are asymmetries in perception. Moreover, we instantiated and tested our model via a real HRI scenario. The experiment consisted of a cooperative game in which participants had to build towers of Lego bricks, while the robot took the role of a suggester. In particular, we conducted experiments using two different robot behaviours. In one condition, based on shared perception, the robot gave suggestions by considering the partners' point of view and using its inference about their common ground to select the most informative hint. In the other condition, the robot just indicated the brick that would have yielded a higher score from its individual perspective. The adoption of shared perception in the selection of suggestions led to better performances in all the instances of the game where the visual information was not a priori common to both agents. However, the subjective evaluation of the robot's behaviour did not change between conditions.

Human- or object-like? Cognitive anthropomorphism of humanoid robots.

Across three experiments (N = 302), we explored whether people cognitively elaborate humanoid robots as human- or object-like. In doing so, we relied on the inversion paradigm, which is an experimental procedure extensively used by cognitive research to investigate the elaboration of social (vs. non-social) stimuli. Overall, mixed-model analyses revealed that full-bodies of humanoid robots were subjected to the inversion effect (body-inversion effect) and, thus, followed a configural processing similar to that activated for human beings. Such a pattern of finding emerged regardless of the similarity of the considered humanoid robots to human beings. That is, it occurred when considering bodies of humanoid robots with medium (Experiment 1), high and low (Experiment 2) levels of human likeness. Instead, Experiment 3 revealed that only faces of humanoid robots with high (vs. low) levels of human likeness were subjected to the inversion effects and, thus, cognitively anthropomorphized. Theoretical and practical implications of these findings for robotic and psychological research are discussed.

A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks.

One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.

Corrigendum: A Socially Adaptable Framework for Human-Robot Interaction.

[This corrects the article DOI: 10.3389/frobt.2020.00121.].

Machines Like Us and People Like You: Toward Human-Robot Shared Experience.

In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, construction, entertainment, sports, and many others. However, current social robotics is still far from the general abilities we expect in a robot collaborator. This limitation is more evident when robots are faced with real-life contexts and activities occurring over long periods. In this article, we argue that human-robot collaboration is more than just being able to work side by side on complementary tasks: collaboration is a complex relational process that entails mutual understanding and reciprocal adaptation. Drawing on this assumption, we propose to shift the focus from "human-robot interaction" to "human-robot shared experience." We hold that for enabling the emergence of such shared experiential space between humans and robots, constructs such as coadaptation, intersubjectivity, individual differences, and identity should become the central focus of modeling. Finally, we suggest that this shift in perspective would imply changing current mainstream design approaches, which are mainly focused on functional aspects of the human-robot interaction, to the development of architectural frameworks that integrate the enabling dimensions of social cognition.

Crocetin Mitigates Irradiation Injury in an In Vitro Model of the Pubertal Testis: Focus on Biological Effects and Molecular Mechanisms.

Infertility is a potential side effect of radiotherapy and significantly affects the quality of life for adolescent cancer survivors. Very few studies have addressed in pubertal models the mechanistic events that could be targeted to provide protection from gonadotoxicity and data on potential radioprotective treatments in this peculiar period of life are elusive. In this study, we utilized an in vitro model of the mouse pubertal testis to investigate the efficacy of crocetin to counteract ionizing radiation (IR)-induced injury and potential underlying mechanisms. Present experiments provide evidence that exposure of testis fragments from pubertal mice to 2 Gy X-rays induced extensive structural and cellular damage associated with overexpression of PARP1, PCNA, SOD2 and HuR and decreased levels of SIRT1 and catalase. A twenty-four hr exposure to 50 µM crocetin pre- and post-IR significantly reduced testis injury and modulated the response to DNA damage and oxidative stress. Nevertheless, crocetin treatment did not counteract the radiation-induced changes in the expression of SIRT1, p62 and LC3II. These results increase the knowledge of mechanisms underlying radiation damage in pubertal testis and establish the use of crocetin as a fertoprotective agent against IR deleterious effects in pubertal period.

Emerging Adults' Expectations About the Next Generation of Robots: Exploring Robotic Needs Through a Latent Profile Analysis.

The investigation of emerging adults' expectations of development of the next generation of robots is a fundamental challenge to narrow the gap between expectations and real technological advances, which can potentially impact the effectiveness of future interactions between humans and robots. Furthermore, the literature highlights the important role played by negative attitudes toward robots in setting people's expectations. To better explore these expectations, we administered the Scale for Robotic Needs and performed a latent profile analysis to describe different expectation profiles about the development of future robots. The profiles identified through this methodology can be placed along a continuum of robots' humanization: from a group that desires mainly the technical features to a group that imagines a humanized robot in the future. Finally, the analysis of emerging adults' knowledge about robots and their negative attitudes toward robots allowed us to understand how these affect their expectations.

Regulatory Functions of L-Carnitine, Acetyl, and Propionyl L-Carnitine in a PCOS Mouse Model: Focus on Antioxidant/Antiglycative Molecular Pathways in the Ovarian Microenvironment.

Polycystic ovary syndrome (PCOS) is a complex metabolic disorder associated with female infertility. Based on energy and antioxidant regulatory functions of carnitines, we investigated whether acyl-L-carnitines improve PCOS phenotype in a mouse model induced by dehydroepiandrosterone (DHEA). CD1 mice received DHEA for 20 days along with two different carnitine formulations: one containing L-carnitine (LC) and acetyl-L-carnitine (ALC), and the other one containing also propionyl-L-carnitine (PLC). We evaluated estrous cyclicity, testosterone level, ovarian follicle health, ovulation rate and oocyte quality, collagen deposition, lipid droplets, and 17ß-HSD IV (17 beta-hydroxysteroid dehydrogenase type IV) expression. Moreover, we analyzed protein expression of SIRT1, SIRT3, SOD2 (superoxide dismutase 2), mitochondrial transcriptional factor A (mtTFA), RAGE (receptor for AGEs), GLO2 (glyoxalase 2) and ovarian accumulation of MG-AGEs (advanced glycation end-products formed by methylglyoxal). Both carnitine formulations ameliorated ovarian PCOS phenotype and positively modulated antioxidant molecular pathways in the ovarian microenvironment. Addition of PLC to LC-ALC formulation mitigated intraovarian MG-AGE accumulation and increased mtTFA expression. In conclusion, our study supports the hypothesis that oral administration of acyl-L-carnitines alleviates ovarian dysfunctions associated with this syndrome and that co-administration of PLC provides better activity. Molecular mechanisms underlying these effects include anti-oxidant/glycative activity and potentiation of mitochondria.

Methylglyoxal-Dependent Glycative Stress and Deregulation of SIRT1 Functional Network in the Ovary of PCOS Mice.

Advanced glycation end-products (AGEs) are involved in the pathogenesis and consequences of polycystic ovary syndrome (PCOS), a complex metabolic disorder associated with female infertility. The most powerful AGE precursor is methylglyoxal (MG), a byproduct of glycolysis, that is detoxified by the glyoxalase system. By using a PCOS mouse model induced by administration of dehydroepiandrosterone (DHEA), we investigated whether MG-dependent glycative stress contributes to ovarian PCOS phenotype and explored changes in the Sirtuin 1 (SIRT1) functional network regulating mitochondrial functions and cell survival. In addition to anovulation and reduced oocyte quality, DHEA ovaries revealed altered collagen deposition, increased vascularization, lipid droplets accumulation and altered steroidogenesis. Here we observed increased intraovarian MG-AGE levels in association with enhanced expression of receptor for AGEs (RAGEs) and deregulation of the glyoxalase system, hallmarks of glycative stress. Moreover, DHEA mice exhibited enhanced ovarian expression of SIRT1 along with increased protein levels of SIRT3 and superoxide dismutase 2 (SOD2), and decreased peroxisome proliferator-activated receptor gamma co-activator 1 alpha (PGC1α), mitochondrial transcriptional factor A (mtTFA) and translocase of outer mitochondrial membrane 20 (TOMM20). Finally, the presence of autophagy protein markers and increased AMP-activated protein kinase (AMPK) suggested the involvement of SIRT1/AMPK axis in autophagy activation. Overall, present findings demonstrate that MG-dependent glycative stress is involved in ovarian dysfunctions associated to PCOS and support the hypothesis of a SIRT1-dependent adaptive response.

A Socially Adaptable Framework for Human-Robot Interaction.

In our everyday lives we regularly engage in complex, personalized, and adaptive interactions with our peers. To recreate the same kind of rich, human-like interactions, a social robot should be aware of our needs and affective states and continuously adapt its behavior to them. Our proposed solution is to have the robot learn how to select the behaviors that would maximize the pleasantness of the interaction for its peers. To make the robot autonomous in its decision making, this process could be guided by an internal motivation system. We wish to investigate how an adaptive robotic framework of this kind would function and personalize to different users. We also wish to explore whether the adaptability and personalization would bring any additional richness to the human-robot interaction (HRI), or whether it would instead bring uncertainty and unpredictability that would not be accepted by the robot's human peers. To this end, we designed a socially adaptive framework for the humanoid robot iCub. As a result, the robot perceives and reuses the affective and interactive signals from the person as input for the adaptation based on internal social motivation. We strive to investigate the value of the generated adaptation in our framework in the context of HRI. In particular, we compare how users will experience interaction with an adaptive versus a non-adaptive social robot. To address these questions, we propose a comparative interaction study with iCub whereby users act as the robot's caretaker, and iCub's social adaptation is guided by an internal comfort level that varies with the stimuli that iCub receives from its caretaker. We investigate and compare how iCub's internal dynamics would be perceived by people, both in a condition when iCub does not personalize its behavior to the person, and in a condition where it is instead adaptive. Finally, we establish the potential benefits that an adaptive framework could bring to the context of repeated interactions with a humanoid robot.

Human Motion Understanding for Selecting Action Timing in Collaborative Human-Robot Interaction.

In the industry of the future, so as in healthcare and at home, robots will be a familiar presence. Since they will be working closely with human operators not always properly trained for human-machine interaction tasks, robots will need the ability of automatically adapting to changes in the task to be performed or to cope with variations in how the human partner completes the task. The goal of this work is to make a further step toward endowing robot with such capability. To this purpose, we focus on the identification of relevant time instants in an observed action, called dynamic instants, informative on the partner's movement timing, and marking instants where an action starts or ends, or changes to another action. The time instants are temporal locations where the motion can be ideally segmented, providing a set of primitives that can be used to build a temporal signature of the action and finally support the understanding of the dynamics and coordination in time. We validate our approach in two contexts, considering first a situation in which the human partner can perform multiple different activities, and then moving to settings where an action is already recognized and shows a certain degree of periodicity. In the two contexts we address different challenges. In the first one, working in batch on a dataset collecting videos of a variety of cooking activities, we investigate whether the action signature we compute could facilitate the understanding of which type of action is occurring in front of the observer, with tolerance to viewpoint changes. In the second context, we evaluate online on the robot iCub the capability of the action signature in providing hints to establish an actual temporal coordination during the interaction with human participants. In both cases, we show promising results that speak in favor of the potentiality of our approach.

Can a Robot Catch You Lying? A Machine Learning System to Detect Lies During Interactions.

Deception is a complex social skill present in human interactions. Many social professions such as teachers, therapists and law enforcement officers leverage on deception detection techniques to support their work activities. Robots with the ability to autonomously detect deception could provide an important aid to human-human and human-robot interactions. The objective of this work is to demonstrate the possibility to develop a lie detection system that could be implemented on robots. To this goal, we focus on human and human robot interaction to understand if there is a difference in the behavior of the participants when lying to a robot or to a human. Participants were shown short movies of robberies and then interrogated by a human and by a humanoid robot "detectives." According to the instructions, subjects provided veridical responses to half of the question and false replies to the other half. Behavioral variables such as eye movements, time to respond and eloquence were measured during the task, while personality traits were assessed before experiment initiation. Participant's behavior showed strong similarities during the interaction with the human and the humanoid. Moreover, the behavioral features were used to train and test a lie detection algorithm. The results show that the selected behavioral variables are valid markers of deception both in human-human and in human-robot interactions and could be exploited to effectively enable robots to detect lies.

Revisiting the body-schema concept in the context of whole-body postural-focal dynamics.

The body-schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert) but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the equilibrium-point hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory-motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability.

Stabilization strategies for unstable dynamics.

The stabilization of the human standing posture was originally attributed to the stiffness of the ankle muscles but direct measurements of the ankle stiffness ruled out this hypothesis, leaving open the possibility for a feedback stabilization strategy driven by proprioceptive signals. This solution, however, could be implemented with two different kinds of control mechanisms, namely continuous or intermittent feedback. The debate is now settled and the latter solution seems to be the most plausible one. Moreover, stabilization of unstable dynamics is not limited to bipedal standing. Indeed many manipulation tasks can be described in the same framework and thus a very general protocol for addressing this kind of problems is the use of haptic virtual reality where instability is generated by some kind of divergent or saddle-like force field. Several studies demonstrated that human subjects can choose to adopt a stiffness or feedback strategy as a combination of biomechanical and task constraints and can learn to switch from one strategy to the other if it is feasible or to use one or the other is infeasible. Understanding such mechanisms is relevant, for example, for the design of novel ergonomic man-machine interfaces in difficult, unstable tasks.

Staring us in the face? An embodied theory of innate face preference.

Human expertise in face perception grows over development, but even within minutes of birth, infants exhibit an extraordinary sensitivity to face-like stimuli. The dominant theory accounts for innate face detection by proposing that the neonate brain contains an innate face detection device, dubbed 'Conspec'. Newborn face preference has been promoted as some of the strongest evidence for innate knowledge, and forms a canonical stage for the modern form of the nature-nurture debate in psychology. Interpretation of newborn face preference results has concentrated on monocular stimulus properties, with little mention or focused investigation of potential binocular involvement. However, the question of whether and how newborns integrate the binocular visual streams bears directly on the generation of observable visual preferences. In this theoretical paper, we employ a synthetic approach utilizing robotic and computational models to draw together the threads of binocular integration and face preference in newborns, and demonstrate cases where the former may explain the latter. We suggest that a system-level view considering the binocular embodiment of newborn vision may offer a mutually satisfying resolution to some long-running arguments in the polarizing debate surrounding the existence and causal structure of newborns' 'innate knowledge' of faces.